IT teams relying on NETSCOUT can now give AI agents direct access to the company's real-time network evidence at runtime, after the company added Model Context Protocol support to its Omnis AI Insights solution.
The announcement, made on 22 September, centres on NETSCOUT's Adaptive Service Intelligence technology, which the company has been refining for over two decades. Rather than passing raw telemetry to AI models, NETSCOUT performs semantic extraction and context optimisation at source, producing what it calls AI-Ready Smart Data before the information enters any downstream system. The new MCP server sits inside Omnis Streamer, one of two core components alongside Omnis Sensor, and exposes that curated intelligence to any compatible AI assistant or agent on demand.
Omnis Sensor processes traffic at critical network vantage points, capturing application context, transaction data and behavioural signals at the moment of observation. Omnis Streamer collects the resulting metadata and, via customisable playbooks, shapes it for specific domains including healthcare, financial services and telecommunications. Direct platform integrations with Splunk, ELK Stack, Datadog, ServiceNow and Dynatrace were already in place; the MCP server adds a runtime path that lets agents query the same intelligence without a pre-built integration.
"Everyone knows there is no value to conclusions that cannot be trusted," said Phil Gray, AVP of product management at NETSCOUT. "By adding MCP tools alongside our existing Kafka streaming capabilities, Omnis AI Insights gives IT professionals the flexibility to feed AI-ready Smart Data into analytics and AI platforms at scale and cost effectively, while also making that same context-rich intelligence directly accessible to Models and Agents."
The company cites a live deployment in which conventional monitoring tools reported no application errors while underlying network conditions were degrading user experience. NETSCOUT's Smart Data retained the specific minimum window size, retransmit count and zero-window event count that explained the degradation. That kind of granular, time-stamped evidence is what NETSCOUT argues differentiates its data foundation from the aggregated telemetry that AI models typically receive.
The practical case for compact, pre-enriched network data is straightforward: AI models fed fragmented operational signals spend more tokens, produce less reliable outputs and are slower to reach actionable conclusions. Reducing that noise at the collection layer rather than at inference is the architectural bet NETSCOUT is making. Whether the MCP server becomes a meaningful distribution channel depends on how quickly enterprise AI agents mature enough to use it. The MCP path avoids requiring customers to migrate infrastructure and works alongside the existing Kafka streaming path.
NETSCOUT is listed on NASDAQ under the ticker NTCT.
To stay across the latest in cloud, AI and enterprise tech analysis from Compare the Cloud, subscribe to our weekly newsletter at https://www.comparethecloud.net/newsletter
